近年來由於社群網路的發展，打卡軟體也漸漸成為熱門軟體，提供地點推薦系統推薦給使用者感興趣的地點就是一個很熱門的研究題材。在地點推薦系統中，地點的數量會比使用者的數量來的多很多，因此要從龐大的地點數量中成功預測使用者感興趣的地點將會是一個很大的挑戰。首先我們利用地理區域性的特色挑選合適的地點作為候選集合，並且提出個人化配置的線性組合方式整合了memory-based CF、社群網路以及地點的流行程度三個層面，能夠根據每一個使用者的特性來推薦相對應適合該使用者的地點。另外我們也實作了以分類問題為主的模型，logistic regression以及libFM，探討此二個模型在處理地點推薦系統下的效能。實驗結果顯示本篇論文所提出的個人化配置的推薦系統能夠達到最佳的效能，並且和分類問題為主的logistic regression和libFM模型相比較下，效能以及效率都較為優秀。 Recently, location-based social network service has become very popular. Point of interests (POI) recommendation service is a promising and interesting research problem. In POI recommendation, numbers of locations are more than numbers of users, so it is a challenge to recommend interest locations from amount of location sets. Our idea is to personally incorporate user preference, social influence and attraction of locations in the recommendation. First, we use geographic influence for candidate selection. Furthermore, we propose a unified POI recommendation framework CLW, which fuses user preference to a POI with social influence and attraction of locations. In addition, we discuss performance of classification model for POI recommendation, logistic regression and libFM. Experimental results shows unified POI recommendation framework CLW outperforms other approaches.
